arXiv:2604.00947v1 Announce Type: cross Abstract: Since the random language model was proposed by E. DeGiuli [Phys. Rev. Lett. 122, 128301], language models have been investigated intensively from the viewpoint of statistical mechanics. Recently, the existence of a Berezinskii–Kosterlitz–Thouless transition was numerically demonstrated in models with long-range interactions between symbols. In statistical mechanics, it has long been known that long-range interactions can induce phase transitions. Therefore, it has remained unclear whether phase transitions observed in language models originate from […]

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Demis: “[to get to AGI] maybe there’s one or two big innovations needed” Sam: “everything based off what we see today is that it will happen.” Ilya: “But is the belief really that if you just 100x the scale, everything would be transformed? I don’t think that’s true.” Dario: “If you just kind of like eyeball the rate at which these capabilities are increasing, it does make you think that we’ll get there by 2026 or 2027.” Jerry: […]

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Prompt Chaining: When One Prompt Isn’t Enough If you’ve ever tried to squeeze an entire project into one prompt—requirements → solution → plan → risks → final doc—you already know how it ends: it skips steps, it forgets constraints, it gives you a “confident” answer you can’t easily verify, and the moment something is wrong, you have no idea where the mistake happened. Prompt Chaining is the fix. Think of it as building a workflow where each prompt […]

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High-Level Synthesis (HLS) is a pivotal electronic design automation (EDA) technology that enables the generation of hardware circuits from high-level language descriptions. A critical step in HLS is Design Space Exploration (DSE), which seeks to identify high-quality hardware architectures under given constraints. However, the enormous size of the design space makes DSE computationally prohibitive. Although numerous algorithms have been proposed to accelerate DSE, our extensive experimental studies reveal that no single algorithm consistently achieves Pareto dominance across all […]

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Clarity doesn’t come from thinking; it comes from movement. The correct order is: Decide what you want to change Anchor it to a why that carries weight Let the RAS filter reality Let the how reveal itself through action What + Why (RAS) → How This is the neuroscience of momentum. – You know what you want to change. You’ve known for a while. You just haven’t moved on it. Your life works, and that’s part of the […]

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The evaluation of supervised machine learning models is a critical stage in the development of reliable predictive systems. Despite the widespread availability of machine learning libraries and automated workflows, model assessment is often reduced to the reporting of a small set of aggregate metrics, which can lead to misleading conclusions about real-world performance. This paper examines the principles, challenges, and practical considerations involved in evaluating supervised learning algorithms across classification and regression tasks. In particular, it discusses how […]

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arXiv:2602.03283v1 Announce Type: cross Abstract: We propose an orthogonal approximate message passing (OAMP) algorithm for signal estimation in the rectangular spiked matrix model with general rotationally invariant (RI) noise. We establish a rigorous state evolution that exactly characterizes the high-dimensional dynamics of the algorithm. Building on this framework, we derive an optimal variant of OAMP that minimizes the predicted mean-squared error at each iteration. For the special case of i.i.d. Gaussian noise, the fixed point of the proposed […]

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arXiv:2601.09730v1 Announce Type: new Abstract: Clinical document metadata, such as document type, structure, author role, medical specialty, and encounter setting, is essential for accurate interpretation of information captured in clinical documents. However, vast documentation heterogeneity and drift over time challenge harmonization of document metadata. Automated extraction methods have emerged to coalesce metadata from disparate practices into target schema. This scoping review aims to catalog research on clinical document metadata extraction, identify methodological trends and applications, and highlight gaps. […]

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A basic unanswered question in neural network training is: what is the best learning rate schedule shape for a given workload? The choice of learning rate schedule is a key factor in the success or failure of the training process, but beyond having some kind of warmup and decay, there is no consensus on what makes a good schedule shape. To answer this question, we designed a search procedure to find the best shapes within a parameterized schedule […]

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arXiv:2601.14235v1 Announce Type: cross Abstract: The Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, […]

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